17 Oct 15:09

charris

ff3df08

v1.17.3

NumPy 1.17.3 Release Notes

This release contains fixes for bugs reported against NumPy 1.17.2 along with a
some documentation improvements. The Python versions supported in this release
are 3.5-3.8.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Highlights

Wheels for Python 3.8
Boolean matmul fixed to use booleans instead of integers.

Compatibility notes

The seldom used PyArray_DescrCheck macro has been changed/fixed.

Contributors

A total of 7 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

Allan Haldane
Charles Harris
Kevin Sheppard
Matti Picus
Ralf Gommers
Sebastian Berg
Warren Weckesser

Pull requests merged

A total of 12 pull requests were merged for this release.

#14456: MAINT: clean up pocketfft modules inside numpy.fft namespace.
#14463: BUG: random.hypergeometic assumes npy_long is npy_int64, hung...
#14502: BUG: random: Revert gh-14458 and refix gh-14557.
#14504: BUG: add a specialized loop for boolean matmul.
#14506: MAINT: Update pytest version for Python 3.8
#14512: DOC: random: fix doc linking, was referencing private submodules.
#14513: BUG,MAINT: Some fixes and minor cleanup based on clang analysis
#14515: BUG: Fix randint when range is 2**32
#14519: MAINT: remove the entropy c-extension module
#14563: DOC: remove note about Pocketfft license file (non-existing here).
#14578: BUG: random: Create a legacy implementation of random.binomial.
#14687: BUG: properly define PyArray_DescrCheck

Checksums

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Assets 6

07 Sep 00:28

charris

v1.17.2

fce34f5

v1.17.2

NumPy 1.17.2 Release Notes

This release contains fixes for bugs reported against NumPy 1.17.1 along with a
some documentation improvements. The most important fix is for lexsort when the
keys are of type (u)int8 or (u)int16. If you are currently using 1.17 you
should upgrade.

The Python versions supported in this release are 3.5-3.7, Python 2.7 has been
dropped. Python 3.8b4 should work with the released source packages, but there
are no future guarantees.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture. The NumPy wheels
on PyPI are built from the OpenBLAS development branch in order to avoid those
errors.

Contributors

A total of 7 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

CakeWithSteak +
Charles Harris
Dan Allan
Hameer Abbasi
Lars Grueter
Matti Picus
Sebastian Berg

Pull requests merged

A total of 8 pull requests were merged for this release.

#14418: BUG: Fix aradixsort indirect indexing.
#14420: DOC: Fix a minor typo in dispatch documentation.
#14421: BUG: test, fix regression in converting to ctypes
#14430: BUG: Do not show Override module in private error classes.
#14432: BUG: Fixed maximum relative error reporting in assert_allclose.
#14433: BUG: Fix uint-overflow if padding with linear_ramp and negative...
#14436: BUG: Update 1.17.x with 1.18.0-dev pocketfft.py.
#14446: REL: Prepare for NumPy 1.17.2 release.

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Assets 6

28 Aug 02:15

charris

v1.16.5

cbdc3b7

v1.16.5

NumPy 1.16.5 Release Notes

The NumPy 1.16.5 release fixes bugs reported against the 1.16.4 release, and
also backports several enhancements from master that seem appropriate for a
release series that is the last to support Python 2.7. The wheels on PyPI are
linked with OpenBLAS v0.3.7-dev, which should fix errors on Skylake series
cpus.

Downstream developers building this release should use Cython >= 0.29.2 and, if
using OpenBLAS, OpenBLAS >= v0.3.7. The supported Python versions are 2.7 and
3.5-3.7.

Contributors

A total of 18 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

Alexander Shadchin
Allan Haldane
Bruce Merry +
Charles Harris
Colin Snyder +
Dan Allan +
Emile +
Eric Wieser
Grey Baker +
Maksim Shabunin +
Marten van Kerkwijk
Matti Picus
Peter Andreas Entschev +
Ralf Gommers
Richard Harris +
Sebastian Berg
Sergei Lebedev +
Stephan Hoyer

Pull requests merged

A total of 23 pull requests were merged for this release.

#13742: ENH: Add project URLs to setup.py
#13823: TEST, ENH: fix tests and ctypes code for PyPy
#13845: BUG: use npy_intp instead of int for indexing array
#13867: TST: Ignore DeprecationWarning during nose imports
#13905: BUG: Fix use-after-free in boolean indexing
#13933: MAINT/BUG/DOC: Fix errors in _add_newdocs
#13984: BUG: fix byte order reversal for datetime64[ns]
#13994: MAINT,BUG: Use nbytes to also catch empty descr during allocation
#14042: BUG: np.array cleared errors occured in PyMemoryView_FromObject
#14043: BUG: Fixes for Undefined Behavior Sanitizer (UBSan) errors.
#14044: BUG: ensure that casting to/from structured is properly checked.
#14045: MAINT: fix histogram*d dispatchers
#14046: BUG: further fixup to histogram2d dispatcher.
#14052: BUG: Replace contextlib.suppress for Python 2.7
#14056: BUG: fix compilation of 3rd party modules with Py_LIMITED_API...
#14057: BUG: Fix memory leak in dtype from dict contructor
#14058: DOC: Document array_function at a higher level.
#14084: BUG, DOC: add new recfunctions to __all__
#14162: BUG: Remove stray print that causes a SystemError on python 3.7
#14297: TST: Pin pytest version to 5.0.1.
#14322: ENH: Enable huge pages in all Linux builds
#14346: BUG: fix behavior of structured_to_unstructured on non-trivial...
#14382: REL: Prepare for the NumPy 1.16.5 release.

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SHA256

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Assets 6

27 Aug 00:58

charris

v1.17.1

2975946

v1.17.1

NumPy 1.17.1 Release Notes

This release contains a number of fixes for bugs reported against NumPy 1.17.0
along with a few documentation and build improvements. The Python versions
supported are 3.5-3.7, note that Python 2.7 has been dropped. Python 3.8b3
should work with the released source packages, but there are no future
guarantees.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture. The NumPy wheels
on PyPI are built from the OpenBLAS development branch in order to avoid those
problems.

Contributors

A total of 17 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

Alexander Jung +
Allan Haldane
Charles Harris
Eric Wieser
Giuseppe Cuccu +
Hiroyuki V. Yamazaki
Jérémie du Boisberranger
Kmol Yuan +
Matti Picus
Max Bolingbroke +
Maxwell Aladago +
Oleksandr Pavlyk
Peter Andreas Entschev
Sergei Lebedev
Seth Troisi +
Vladimir Pershin +
Warren Weckesser

Pull requests merged

A total of 24 pull requests were merged for this release.

#14156: TST: Allow fuss in testing strided/non-strided exp/log loops
#14157: BUG: avx2_scalef_ps must be static
#14158: BUG: Remove stray print that causes a SystemError on python 3.7.
#14159: BUG: Fix DeprecationWarning in python 3.8.
#14160: BLD: Add missing gcd/lcm definitions to npy_math.h
#14161: DOC, BUILD: cleanups and fix (again) 'build dist'
#14166: TST: Add 3.8-dev to travisCI testing.
#14194: BUG: Remove the broken clip wrapper (Backport)
#14198: DOC: Fix hermitian argument docs in svd.
#14199: MAINT: Workaround for Intel compiler bug leading to failing test
#14200: TST: Clean up of test_pocketfft.py
#14201: BUG: Make advanced indexing result on read-only subclass writeable...
#14236: BUG: Fixed default BitGenerator name
#14237: ENH: add c-imported modules for freeze analysis in np.random
#14296: TST: Pin pytest version to 5.0.1
#14301: BUG: Fix leak in the f2py-generated module init and PyMem_Del...
#14302: BUG: Fix formatting error in exception message
#14307: MAINT: random: Match type of SeedSequence.pool_size to DEFAULT_POOL_SIZE.
#14308: BUG: Fix numpy.random bug in platform detection
#14309: ENH: Enable huge pages in all Linux builds
#14330: BUG: Fix segfault in random.permutation(x) when x is a string.
#14338: BUG: don't fail when lexsorting some empty arrays (#14228)
#14339: BUG: Fix misuse of .names and .fields in various places (backport...
#14345: BUG: fix behavior of structured_to_unstructured on non-trivial...
#14350: REL: Prepare 1.17.1 release

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Assets 6

26 Jul 18:46

charris

v1.17.0

d9b1e32

v1.17.0

NumPy 1.17.0 Release Notes

This NumPy release contains a number of new features that should substantially
improve its performance and usefulness, see Highlights below for a summary. The
Python versions supported are 3.5-3.7, note that Python 2.7 has been dropped.
Python 3.8b2 should work with the released source packages, but there are no
future guarantees.

Downstream developers should use Cython >= 0.29.11 for Python 3.8 support and
OpenBLAS >= 3.7 (not currently out) to avoid problems on the Skylake
architecture. The NumPy wheels on PyPI are built from the OpenBLAS development
branch in order to avoid those problems.

Highlights

A new extensible random module along with four selectable random number generators <random.BitGenerators> and improved seeding designed for use in parallel
processes has been added. The currently available bit generators are MT19937 <random.mt19937.MT19937>, PCG64 <random.pcg64.PCG64>, Philox <random.philox.Philox>, and SFC64 <random.sfc64.SFC64>. See below under
New Features.
NumPy's FFT <fft> implementation was changed from fftpack to pocketfft,
resulting in faster, more accurate transforms and better handling of datasets
of prime length. See below under Improvements.
New radix sort and timsort sorting methods. It is currently not possible to
choose which will be used. They are hardwired to the datatype and used
when either stable or mergesort is passed as the method. See below
under Improvements.
Overriding numpy functions is now possible by default,
see __array_function__ below.

New functions

numpy.errstate is now also a function decorator

Deprecations

`numpy.polynomial` functions warn when passed `float` in place of `int`

Previously functions in this module would accept float values provided they
were integral (1.0, 2.0, etc). For consistency with the rest of numpy,
doing so is now deprecated, and in future will raise a TypeError.

Similarly, passing a float like 0.5 in place of an integer will now raise a
TypeError instead of the previous ValueError.

Deprecate `numpy.distutils.exec_command` and `temp_file_name`

The internal use of these functions has been refactored and there are better
alternatives. Replace exec_command with subprocess.Popen and
temp_file_name <numpy.distutils.exec_command> with tempfile.mkstemp.

Writeable flag of C-API wrapped arrays

When an array is created from the C-API to wrap a pointer to data, the only
indication we have of the read-write nature of the data is the writeable
flag set during creation. It is dangerous to force the flag to writeable.
In the future it will not be possible to switch the writeable flag to True
from python.
This deprecation should not affect many users since arrays created in such
a manner are very rare in practice and only available through the NumPy C-API.

`numpy.nonzero` should no longer be called on 0d arrays

The behavior of numpy.nonzero on 0d arrays was surprising, making uses of it
almost always incorrect. If the old behavior was intended, it can be preserved
without a warning by using nonzero(atleast_1d(arr)) instead of
nonzero(arr). In a future release, it is most likely this will raise a
ValueError.

Writing to the result of `numpy.broadcast_arrays` will warn

Commonly numpy.broadcast_arrays returns a writeable array with internal
overlap, making it unsafe to write to. A future version will set the
writeable flag to False, and require users to manually set it to
True if they are sure that is what they want to do. Now writing to it will
emit a deprecation warning with instructions to set the writeable flag
True. Note that if one were to inspect the flag before setting it, one
would find it would already be True. Explicitly setting it, though, as one
will need to do in future versions, clears an internal flag that is used to
produce the deprecation warning. To help alleviate confusion, an additional
FutureWarning will be emitted when accessing the writeable flag state to
clarify the contradiction.

Note that for the C-side buffer protocol such an array will return a
readonly buffer immediately unless a writable buffer is requested. If
a writeable buffer is requested a warning will be given. When using
cython, the const qualifier should be used with such arrays to avoid
the warning (e.g. cdef const double[::1] view).

Future Changes

Shape-1 fields in dtypes won't be collapsed to scalars in a future version

Currently, a field specified as [(name, dtype, 1)] or "1type" is
interpreted as a scalar field (i.e., the same as [(name, dtype)] or
[(name, dtype, ()]). This now raises a FutureWarning; in a future version,
it will be interpreted as a shape-(1,) field, i.e. the same as [(name, dtype, (1,))] or "(1,)type" (consistently with [(name, dtype, n)]
/ "ntype" with n>1, which is already equivalent to [(name, dtype, (n,)] / "(n,)type").

Compatibility notes

`float16` subnormal rounding

Casting from a different floating point precision to float16 used incorrect
rounding in some edge cases. This means in rare cases, subnormal results will
now be rounded up instead of down, changing the last bit (ULP) of the result.

Signed zero when using divmod

Starting in version 1.12.0, numpy incorrectly returned a negatively signed zero
when using the divmod and floor_divide functions when the result was
zero. For example::

   >>> np.zeros(10)//1
   array([-0., -0., -0., -0., -0., -0., -0., -0., -0., -0.])

With this release, the result is correctly returned as a positively signed
zero::

   >>> np.zeros(10)//1
   array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

`MaskedArray.mask` now returns a view of the mask, not the mask itself

Returning the mask itself was unsafe, as it could be reshaped in place which
would violate expectations of the masked array code. The behavior of mask <ma.MaskedArray.mask> is now consistent with data <ma.MaskedArray.data>,
which also returns a view.

The underlying mask can still be accessed with ._mask if it is needed.
Tests that contain assert x.mask is not y.mask or similar will need to be
updated.

Do not lookup `buffer` attribute in `numpy.frombuffer`

Looking up __buffer__ attribute in numpy.frombuffer was undocumented and
non-functional. This code was removed. If needed, use
frombuffer(memoryview(obj), ...) instead.

`out` is buffered for memory overlaps in `take`, `choose`, `put`

If the out argument to these functions is provided and has memory overlap with
the other arguments, it is now buffered to avoid order-dependent behavior.

Unpickling while loading requires explicit opt-in

The functions load, and lib.format.read_array take an
allow_pickle keyword which now defaults to False in response to
CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

Potential changes to the random stream in old random module

Due to bugs in the application of log to random floating point numbers,
the stream may change when sampling from ~RandomState.beta, ~RandomState.binomial,
~RandomState.laplace, ~RandomState.logistic, ~RandomState.logseries or
~RandomState.multinomial if a 0 is generated in the underlying MT19937 <~numpy.random.mt11937.MT19937> random stream. There is a 1 in
:math:10^{53} chance of this occurring, so the probability that the stream
changes for any given seed is extremely small. If a 0 is encountered in the
underlying generator, then the incorrect value produced (either numpy.inf or
numpy.nan) is now dropped.

`i0` now always returns a result with the same shape as the input

Previously, the output was squeezed, such that, e.g., input with just a single
element would lead to an array scalar being returned, and inputs with shapes
such as (10, 1) would yield results that would not broadcast against the
input.

Note that we generally recommend the SciPy implementation over the numpy one:
it is a proper ufunc written in C, and more than an order of magnitude faster.

`can_cast` no longer assumes all unsafe casting is allowed

Previously, can_cast returned True for almost all inputs for
casting='unsafe', even for cases where casting was not possible, such as
from a structured dtype to a regular one. This has been fixed, making it
more consistent with actual casting using, e.g., the .astype <ndarray.astype>
method.

`ndarray.flags.writeable` can be switched to true slightly more often

In rare cases, it was not possible to switch an array from not writeable
to writeable, although a base array is writeable. This can happ...

Assets 6

16 Jul 14:47

charris

v1.17.0rc2

eb5dda5

v1.17.0rc2 Pre-release

Pre-release

.. currentmodule:: numpy

==========================
NumPy 1.17.0 Release Notes

Highlights

A new extensible random module along with four selectable random number generators <random.BitGenerators> and improved seeding designed for use in parallel
processes has been added. The currently available bit generators are MT19937 <random.mt19937.MT19937>, PCG64 <random.pcg64.PCG64>, Philox <random.philox.Philox>, and SFC64 <random.sfc64.SFC64>. See below under
New Features.
NumPy's FFT <fft> implementation was changed from fftpack to pocketfft,
resulting in faster, more accurate transforms and better handling of datasets
of prime length. See below under Improvements.
New radix sort and timsort sorting methods. It is currently not possible to
choose which will be used. They are hardwired to the datatype and used
when either stable or mergesort is passed as the method. See below
under Improvements.
Overriding numpy functions is now possible by default,
see __array_function__ below.

New functions

numpy.errstate is now also a function decorator

Deprecations

`numpy.polynomial` functions warn when passed `float` in place of `int`

Similarly, passing a float like 0.5 in place of an integer will now raise a
TypeError instead of the previous ValueError.

Deprecate `numpy.distutils.exec_command` and `temp_file_name`

Writeable flag of C-API wrapped arrays

`numpy.nonzero` should no longer be called on 0d arrays

Writing to the result of `numpy.broadcast_arrays` will warn

Future Changes

Shape-1 fields in dtypes won't be collapsed to scalars in a future version

Compatibility notes

`float16` subnormal rounding

Signed zero when using divmod

Starting in version 1.12.0, numpy incorrectly returned a negatively signed zero
when using the divmod and floor_divide functions when the result was
zero. For example::

   >>> np.zeros(10)//1
   array([-0., -0., -0., -0., -0., -0., -0., -0., -0., -0.])

With this release, the result is correctly returned as a positively signed
zero::

   >>> np.zeros(10)//1
   array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

`MaskedArray.mask` now returns a view of the mask, not the mask itself

The underlying mask can still be accessed with ._mask if it is needed.
Tests that contain assert x.mask is not y.mask or similar will need to be
updated.

Do not lookup `buffer` attribute in `numpy.frombuffer`

Looking up __buffer__ attribute in numpy.frombuffer was undocumented and
non-functional. This code was removed. If needed, use
frombuffer(memoryview(obj), ...) instead.

`out` is buffered for memory overlaps in `take`, `choose`, `put`

If the out argument to these functions is provided and has memory overlap with
the other arguments, it is now buffered to avoid order-dependent behavior.

Unpickling while loading requires explicit opt-in

The functions load, and lib.format.read_array take an
allow_pickle keyword which now defaults to False in response to
CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

.. currentmodule:: numpy.random.mtrand

Potential changes to the random stream in old random module

.. currentmodule:: numpy

`i0` now always returns a result with the same shape as the input

Note that we generally recommend the SciPy implementation over the numpy one:
it is a proper ufunc written in C, and more than an order of magnitude faster.

`can_cast` no longer assumes all unsafe casting is allowed

`ndarray.flags.writeable` can be switched to true slightly more often

In rare cas...

Assets 6

30 Jun 22:20

charris

v1.17.0rc1

9bd91ae

v1.17.0rc1 Pre-release

Pre-release

==========================
NumPy 1.17.0 Release Notes

This NumPy release contains a number of new features that should substantially
improve its performance and usefulness, see Highlights below for a summary. The
Python versions supported are 3.5-3.7, note that Python 2.7 has been dropped.
Python 3.8b1 should work with the released source packages, but there are no
future guarantees.

Downstream developers should use Cython >= 0.29.10 for Python 3.8 support and
OpenBLAS >= 3.7 (not currently out) to avoid problems on the Skylake
architecture. The NumPy wheels on PyPI are built from the OpenBLAS development
branch in order to avoid those problems.

Highlights

A new extensible random module along with four selectable random number
generators and improved seeding designed for use in parallel processes has
been added. The currently available bit generators are MT19937, PCG64,
Philox, and SFC64. See below under New Features.
NumPy's FFT implementation was changed from fftpack to pocketfft, resulting
in faster, more accurate transforms and better handling of datasets of
prime length. See below under Improvements.
New radix sort and timsort sorting methods. It is currently not possible to
choose which will be used, but they are hardwired to the datatype and used
when either stable or mergesort is passed as the method. See below
under Improvements.
Overriding numpy functions is now possible by default,
see __array_function__ below.

New functions

numpy.errstate is now also a function decorator

Deprecations

`np.polynomial` functions warn when passed `float` in place of `int`

Similarly, passing a float like 0.5 in place of an integer will now raise a
TypeError instead of the previous ValueError.

Deprecate `numpy.distutils.exec_command` and `numpy.distutils.temp_file_name`

The internal use of these functions has been refactored and there are better
alternatives. Relace exec_command with subprocess.Popen and
temp_file_name with tempfile.mkstemp.

Writeable flag of C-API wrapped arrays

When an array is created from the C-API to wrap a pointer to data, the only
indication we have of the read-write nature of the data is the writeable
flag set during creation. It is dangerous to force the flag to writeable.
In the future it will not be possible to switch the writeable flag to True
from python. This deprecation should not affect many users since arrays created in such
a manner are very rare in practice and only available through the NumPy C-API.

`numpy.nonzero` should no longer be called on 0d arrays

The behavior of nonzero on 0d arrays was surprising, making uses of it almost
always incorrect. If the old behavior was intended, it can be preserved without
a warning by using nonzero(atleast_1d(arr)) instead of nonzero(arr).
In a future release, it is most likely this will raise a ValueError.

Writing to the result of `numpy.broadcast_arrays` will warn

Future Changes

Shape-1 fields in dtypes won't be collapsed to scalars in a future version

Currently, a field specified as [(name, dtype, 1)] or "1type" is
interpreted as a scalar field (i.e., the same as [(name, dtype)] or
[(name, dtype, ()]). This now raises a FutureWarning; in a future version,
it will be interpreted as a shape-(1,) field, i.e. the same as
[(name,dtype, (1,))] or "(1,)type" (consistent with
[(name, dtype, n)] / "ntype" for n > 1, which is already equivalent to
[(name, dtype,(n,)] / "(n,)type").

Compatibility notes

float16 subnormal rounding

Casting from a different floating point precision to float16 used incorrect
rounding in some edge cases. This means in rare cases, subnormal results will
now be rounded up instead of down, changing the last bit (ULP) of the result.

Signed zero when using divmod

Starting in version 1.12.0, numpy incorrectly returned a negatively signed zero
when using the divmod and floor_divide functions when the result was
zero. For example:

   >>> np.zeros(10)//1
   array([-0., -0., -0., -0., -0., -0., -0., -0., -0., -0.])

With this release, the result is correctly returned as a positively signed
zero:

   >>> np.zeros(10)//1
   array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

`MaskedArray.mask` now returns a view of the mask, not the mask itself

Returning the mask itself was unsafe, as it could be reshaped in place which
would violate expectations of the masked array code. It's behavior is now
consistent with the .data attribute, which also returns a view.

The underlying mask can still be accessed with ._mask if it is needed.
Tests that contain assert x.mask is not y.mask or similar will need to be
updated.

Do not lookup `buffer` attribute in `numpy.frombuffer`

Looking up __buffer__ attribute in numpy.frombuffer was undocumented and
non-functional. This code was removed. If needed, use
frombuffer(memoryview(obj), ...) instead.

`out`is buffered for memory overlaps in `np.take`, `np.choose`, `np.put`

If the out argument to these functions is provided and has memory overlap with
the other arguments, it is now buffered to avoid order-dependent behavior.

Unpickling while loading requires explicit opt-in

The functions np.load, and np.lib.format.read_array take an
allow_pickle keyword which now defaults to False in response to
CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

Potential changes to the random stream in old random module

Due to bugs in the application of log to random floating point numbers,
the stream may change when sampling from np.random.beta, np.random.binomial,
np.random.laplace, np.random.logistic, np.random.logseries or
np.random.multinomial if a 0 is generated in the underlying MT19937 random stream.
There is a 1 in :math:10^{53} chance of this occurring, and so the probability that
the stream changes for any given seed is extremely small. If a 0 is encountered in the
underlying generator, then the incorrect value produced (either np.inf
or np.nan) is now dropped.

`i0` now always returns a result with the same shape as the input

Note that we generally recommend the SciPy implementation over the numpy one:
it is a proper ufunc written in C, and more than an order of magnitude faster.

`np.can_cast` no longer assumes all unsafe casting is allowed

`arr.writeable` can be switched to true slightly more often

In rare cases, it was not possible to switch an array from not writeable
to writeable, although a base array is writeable. This can happen if an
intermediate arr.base object is writeable. Previously, only the deepest
base object was considered for this decision. However, in rare cases this
object does not have the necessary information. In that case switching to
writeable was never allowed. This has now been fixed.

C API changes

dimension or stride input arguments are now passed by `npy_intp const*`

Previously these function arguments were declared as the more strict
``npy_intp*...

Assets 6

28 May 19:17

charris

v1.16.4

7ebbc2e

v1.16.4

==========================
NumPy 1.16.4 Release Notes

The NumPy 1.16.4 release fixes bugs reported against the 1.16.3 release, and
also backports several enhancements from master that seem appropriate for a
release series that is the last to support Python 2.7. The wheels on PyPI are
linked with OpenBLAS v0.3.7-dev, which should fix issues on Skylake series
cpus.

Downstream developers building this release should use Cython >= 0.29.2 and,
if using OpenBLAS, OpenBLAS > v0.3.7. The supported Python versions are 2.7 and
3.5-3.7.

New deprecations

Writeable flag of C-API wrapped arrays

When an array is created from the C-API to wrap a pointer to data, the only
indication we have of the read-write nature of the data is the writeable
flag set during creation. It is dangerous to force the flag to writeable. In
the future it will not be possible to switch the writeable flag to True
from python. This deprecation should not affect many users since arrays
created in such a manner are very rare in practice and only available through
the NumPy C-API.

Compatibility notes

Potential changes to the random stream

Changes

`numpy.lib.recfunctions.structured_to_unstructured` does not squeeze single-field views

Previously structured_to_unstructured(arr[['a']]) would produce a squeezed
result inconsistent with structured_to_unstructured(arr[['a', b']]). This
was accidental. The old behavior can be retained with
structured_to_unstructured(arr[['a']]).squeeze(axis=-1) or far more simply,
arr['a'].

Contributors

A total of 10 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

Charles Harris
Eric Wieser
Dennis Zollo +
Hunter Damron +
Jingbei Li +
Kevin Sheppard
Matti Picus
Nicola Soranzo +
Sebastian Berg
Tyler Reddy

Pull requests merged

A total of 16 pull requests were merged for this release.

#13392: BUG: Some PyPy versions lack PyStructSequence_InitType2.
#13394: MAINT, DEP: Fix deprecated assertEquals()
#13396: BUG: Fix structured_to_unstructured on single-field types (backport)
#13549: BLD: Make CI pass again with pytest 4.5
#13552: TST: Register markers in conftest.py.
#13559: BUG: Removes ValueError for empty kwargs in arraymultiter_new
#13560: BUG: Add TypeError to accepted exceptions in crackfortran.
#13561: BUG: Handle subarrays in descr_to_dtype
#13562: BUG: Protect generators from log(0.0)
#13563: BUG: Always return views from structured_to_unstructured when...
#13564: BUG: Catch stderr when checking compiler version
#13565: BUG: longdouble(int) does not work
#13587: BUG: distutils/system_info.py fix missing subprocess import (#13523)
#13620: BUG,DEP: Fix writeable flag setting for arrays without base
#13641: MAINT: Prepare for the 1.16.4 release.
#13644: BUG: special case object arrays when printing rel-, abs-error

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Assets 6

22 Apr 02:00

charris

v1.16.3

08b17ae

v1.16.3

==========================
NumPy 1.16.3 Release Notes

The NumPy 1.16.3 release fixes bugs reported against the 1.16.2 release, and
also backports several enhancements from master that seem appropriate for a
release series that is the last to support Python 2.7. The wheels on PyPI are
linked with OpenBLAS v0.3.4+, which should fix the known threading issues
found in previous OpenBLAS versions.

Downstream developers building this release should use Cython >= 0.29.2 and,
if using OpenBLAS, OpenBLAS > v0.3.4.

The most noticeable change in this release is that unpickling object arrays
when loading *.npy or *.npz files now requires an explicit opt-in.
This backwards incompatible change was made in response to
CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

Compatibility notes

Unpickling while loading requires explicit opt-in

The functions np.load, and np.lib.format.read_array take an
allow_pickle keyword which now defaults to False in response to
CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

Improvements

Covariance in `random.mvnormal` cast to double

This should make the tolerance used when checking the singular values of the
covariance matrix more meaningful.

Changes

`__array_interface__` offset now works as documented

The interface may use an offset value that was previously mistakenly
ignored.

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Assets 6

26 Feb 19:37

charris

v1.16.2

0eeb158

v1.16.2

==========================
NumPy 1.16.2 Release Notes

NumPy 1.16.2 is a quick release fixing several problems encountered on Windows.
The Python versions supported are 2.7 and 3.5-3.7. The Windows problems
addressed are:

DLL load problems for NumPy wheels on Windows,
distutils command line parsing on Windows.

There is also a regression fix correcting signed zeros produced by divmod, see
below for details.

Downstream developers building this release should use Cython >= 0.29.2 and, if
using OpenBLAS, OpenBLAS > v0.3.4.

If you are installing using pip, you may encounter a problem with older
installed versions of NumPy that pip did not delete becoming mixed with the
current version, resulting in an ImportError. That problem is particularly
common on Debian derived distributions due to a modified pip. The fix is to
make sure all previous NumPy versions installed by pip have been removed. See
#12736 <https://github.com/numpy/numpy/issues/12736>__ for discussion of the
issue.

Compatibility notes

Signed zero when using divmod

Starting in version 1.12.0, numpy incorrectly returned a negatively signed zero
when using the divmod and floor_divide functions when the result was
zero. For example:

   >>> np.zeros(10)//1
   array([-0., -0., -0., -0., -0., -0., -0., -0., -0., -0.])

With this release, the result is correctly returned as a positively signed
zero:

   >>> np.zeros(10)//1
   array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

Contributors

A total of 5 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

Charles Harris
Eric Wieser
Matti Picus
Tyler Reddy
Tony LaTorre +

Pull requests merged

A total of 7 pull requests were merged for this release.

#12909: TST: fix vmImage dispatch in Azure
#12923: MAINT: remove complicated test of multiarray import failure mode
#13020: BUG: fix signed zero behavior in npy_divmod
#13026: MAINT: Add functions to parse shell-strings in the platform-native...
#13028: BUG: Fix regression in parsing of F90 and F77 environment variables
#13038: BUG: parse shell escaping in extra_compile_args and extra_link_args
#13041: BLD: Windows absolute path DLL loading

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Assets 6

Releases: numpy/numpy

v1.17.3

NumPy 1.17.3 Release Notes

Highlights

Compatibility notes

Contributors

Pull requests merged

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v1.17.2

NumPy 1.17.2 Release Notes

Contributors

Pull requests merged

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v1.16.5

NumPy 1.16.5 Release Notes

Contributors

Pull requests merged

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v1.17.1

NumPy 1.17.1 Release Notes

Contributors

Pull requests merged

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v1.17.0

NumPy 1.17.0 Release Notes

Highlights

New functions

Deprecations

numpy.polynomial functions warn when passed float in place of int

Deprecate numpy.distutils.exec_command and temp_file_name

Writeable flag of C-API wrapped arrays

numpy.nonzero should no longer be called on 0d arrays

Writing to the result of numpy.broadcast_arrays will warn

Future Changes

Shape-1 fields in dtypes won't be collapsed to scalars in a future version

Compatibility notes

float16 subnormal rounding

Signed zero when using divmod

MaskedArray.mask now returns a view of the mask, not the mask itself

Do not lookup __buffer__ attribute in numpy.frombuffer

out is buffered for memory overlaps in take, choose, put

Unpickling while loading requires explicit opt-in

Potential changes to the random stream in old random module

i0 now always returns a result with the same shape as the input

can_cast no longer assumes all unsafe casting is allowed

ndarray.flags.writeable can be switched to true slightly more often

v1.17.0rc2

========================== NumPy 1.17.0 Release Notes

Highlights

New functions

Deprecations

numpy.polynomial functions warn when passed float in place of int

Deprecate numpy.distutils.exec_command and temp_file_name

Writeable flag of C-API wrapped arrays

numpy.nonzero should no longer be called on 0d arrays

Writing to the result of numpy.broadcast_arrays will warn

Future Changes

Shape-1 fields in dtypes won't be collapsed to scalars in a future version

Compatibility notes

float16 subnormal rounding

Signed zero when using divmod

MaskedArray.mask now returns a view of the mask, not the mask itself

Do not lookup __buffer__ attribute in numpy.frombuffer

out is buffered for memory overlaps in take, choose, put

Unpickling while loading requires explicit opt-in

Potential changes to the random stream in old random module

i0 now always returns a result with the same shape as the input

can_cast no longer assumes all unsafe casting is allowed

ndarray.flags.writeable can be switched to true slightly more often

v1.17.0rc1

========================== NumPy 1.17.0 Release Notes

Highlights

`numpy.polynomial` functions warn when passed `float` in place of `int`

Deprecate `numpy.distutils.exec_command` and `temp_file_name`

`numpy.nonzero` should no longer be called on 0d arrays

Writing to the result of `numpy.broadcast_arrays` will warn

`float16` subnormal rounding

`MaskedArray.mask` now returns a view of the mask, not the mask itself

Do not lookup `buffer` attribute in `numpy.frombuffer`

`out` is buffered for memory overlaps in `take`, `choose`, `put`

`i0` now always returns a result with the same shape as the input

`can_cast` no longer assumes all unsafe casting is allowed

`ndarray.flags.writeable` can be switched to true slightly more often

==========================
NumPy 1.17.0 Release Notes

`numpy.polynomial` functions warn when passed `float` in place of `int`

Deprecate `numpy.distutils.exec_command` and `temp_file_name`

`numpy.nonzero` should no longer be called on 0d arrays

Writing to the result of `numpy.broadcast_arrays` will warn

`float16` subnormal rounding

`MaskedArray.mask` now returns a view of the mask, not the mask itself

Do not lookup `buffer` attribute in `numpy.frombuffer`

`out` is buffered for memory overlaps in `take`, `choose`, `put`

`i0` now always returns a result with the same shape as the input

`can_cast` no longer assumes all unsafe casting is allowed

`ndarray.flags.writeable` can be switched to true slightly more often

==========================
NumPy 1.17.0 Release Notes

`np.polynomial` functions warn when passed `float` in place of `int`

Deprecate `numpy.distutils.exec_command` and `numpy.distutils.temp_file_name`

`numpy.nonzero` should no longer be called on 0d arrays

Writing to the result of `numpy.broadcast_arrays` will warn

`MaskedArray.mask` now returns a view of the mask, not the mask itself

Do not lookup `buffer` attribute in `numpy.frombuffer`

`out`is buffered for memory overlaps in `np.take`, `np.choose`, `np.put`

`i0` now always returns a result with the same shape as the input

`np.can_cast` no longer assumes all unsafe casting is allowed

`arr.writeable` can be switched to true slightly more often

dimension or stride input arguments are now passed by `npy_intp const*`

==========================
NumPy 1.16.4 Release Notes

`numpy.lib.recfunctions.structured_to_unstructured` does not squeeze single-field views

==========================
NumPy 1.16.3 Release Notes

Covariance in `random.mvnormal` cast to double

`__array_interface__` offset now works as documented

==========================
NumPy 1.16.2 Release Notes